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Input Ranking Revisited: A Theoretical Input Sensitivity Approach

  • Sanggil Kang
  • Can Isik
  • Steven Morphet
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 19)

Abstract

In feedforward neural networks, all inputs contribute to a greater or lesser extent when calculating the outputs. Therefore, inputs may be ordered from the greatest contributor to the least. Input ranking is non-trivial — cursory examination of the weight and bias matrices fails to reveal ranking. Solving the ranking issue allows for elimination of inputs with little influence on output. This paper presents a new method of determining the input sensitivity of three-layer feedforward neural networks. Specifically, sensitivity of an input is independent of the magnitudes of the remaining inputs, providing an unambiguous ranking of input importance. Small changes to influential inputs will result in great changes to output. This concept motivated the theoretical approach to input ranking. Examination of theoretical results will demonstrate the correctness of this approach.

Keywords

Neural Network Hide Neuron Feedforward Neural Network Input Neuron Joint Probability Density Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Sanggil Kang
    • 1
  • Can Isik
    • 1
  • Steven Morphet
    • 1
    • 2
  1. 1.Department of Electrical Engineering and Computer ScienceSyracuse UniversitySyracuseUSA
  2. 2.Syracuse Research CorporationNorth SyracuseUSA

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